Reconstruction of coronary artery centrelines from X-ray angiography using a mixture of student’s t-distributions

Cimen, S., Gooya, A. , Ravikumar, N., Taylor, Z.A. and Frangi, A.F. (2016) Reconstruction of coronary artery centrelines from X-ray angiography using a mixture of student’s t-distributions. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III. Series: Lecture notes in computer science (9902). Springer, pp. 291-299. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_34)

Abstract

Three-dimensional reconstructions of coronary arteries can overcome some of the limitations of 2D X-ray angiography, namely artery overlap/foreshortening and lack of depth information. Model-based arterial reconstruction algorithms usually rely on 2D coronary artery segmentations and require good robustness to outliers. In this paper, we propose a novel probabilistic method to reconstruct coronary artery centrelines from retrospectively gated X-ray images based on a probabilistic mixture model. Specifically, 3D coronary artery centrelines are described by a mixture of Student’s t-distributions, and the reconstruction is formulated as maximum-likelihood estimation of the mixture model parameters, given the 2D segmentations of arteries from 2D X-ray images. Our method provides robustness against the erroneously segmented parts in the 2D segmentations by taking advantage of the inherent robustness of t-distributions. We validate our reconstruction results using synthetic phantom and clinical X-ray angiography data. The results show that the proposed method can cope with imperfect and noisy segmentation data.

• Item Type: Book Sections
• Additional Information: eISBN: 9783319467269.
• Status: Published
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Cimen, S., Gooya, A., Ravikumar, N., Taylor, Z.A., and Frangi, A.F.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783319467252

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